An improved spherical simplex square root unscented Kalman filter (ISRUKF) algorithm based on Gaussian process regression (GPR) was investigated to specifically address unknown or inaccurate models problems

resulting in bad or even divergent filtering results in turbofan engine performance monitoring. The transition and measurement GPR models of turbofan engine dynamic systems were established

and used as a substitute for nonlinear model in unscented Kalman filter (UKF). Meantime

the spherical simplex sampling was exploited to decrease the computational complexity

while square root of measurement residuals covariance matrixes was used instead of variance matrixes during recursive arithmetic process to improve the calculation efficiency and numerical stability in ISRUKF algorithm. Using the GPR

the models were firstly trained and verified

thus overcoming dependency on the previous engine models with the abilities of adaptive noise adjustment and high precision. The gradual and rapid deterioration process of different turbofan engine gas path components were then testified and compared by the extended Kalman filter (EKF)

SRUKF and ISRUKF. The results show that the precision of ISRUKF has reached 99.9%

demonstrating the effectiveness of the ISRUKF for health parameters monitoring of turbofan engine gas path.